Srinivasan, Varun

Varun Srinivasan, PhD, is a product lead and data scientist at Trinnex specializing in solving client problems through development of digital solutions and products. Varun has previously worked as a process engineer and wastewater data scientist where he specialized in process design, innovative wastewater treatment technologies, nutrient removal and recovery, and digital solutions. He obtained his Masters and Doctoral degrees in Environmental Engineering from University of Massachusetts-Amherst where he performed research on a variety of subjects.

Titles from this speaker

Description: A Case for Using Internally Stored Carbon for Intensifying Biological Nutrient...

A Case for Using Internally Stored Carbon for Intensifying Biological Nutrient Removal â€“ Mechanism, Evidence in Full-Scale Facilities, and Process Design

Abstract

Introduction
Total nitrogen removal has historically been limited by the amount of reducing electrons in wastewater to perform denitrification. Most wastewater resource recovery facilities (WRRFs) use an external carbon source such as methanol or acetic acid to provide the electron donor required for denitrification. However, this can be expensive especially with stringent nitrogen limits. For this reason, there has been an increased interest in using internally stored carbon for denitrification. Cycling of internally stored carbon is a key mechanism that has been successfully used for removing phosphorus in the enhanced biological phosphorus removal (EBPR) process. However, a complete understanding of production and usage of internally stored carbon could provide benefits beyond EBPR to also perform total nitrogen removal and biological nutrient removal (BNR).
Mechanism
It has been recognized that bacteria adopt a survival strategy to tackle feast-famine cycles. Figure 1 shows the conceptual metabolic pathway for PHA accumulation. The storage of PHA is largely triggered by the presence of excess (feast - beyond required for growth) soluble substrate such as VFAs and energy (ATP) (Beun et al., 2000; Third et al., 2003). The energy required can be produced through aerobic/anoxic respiration (PHA-accumulating OHOs) or through hydrolysis of polyphosphate (in the case of polyphosphate accumulating organisms - PAOs). Subsequently, when there is a lack of excess VFAs (famine), the PHA produced can either be used for P-uptake under the presence of oxygen (in the case of PAOs) and/or be used for denitrification under carbon-limited conditions by other PHA-accumulating heterotrophs (Basset et al., 2016; Vocks et al., 2005; Zhou et al., 2012). Extended aerobic conditions can result in a depletion of PHA since PHA will also be used for aerobic respiration and growth under famine. However, with the advent of low DO operating strategies, there is potential to minimize PHA utilization for growth and maximize utilization for denitrification. The other carbon storage polymer that has been implicated in providing denitrification potential is glycogen (Coats et al., 2011). In a BNR process, glycogen is typically used up under anaerobic or feast conditions to generate reducing power to uptake volatile fatty acids (VFAs) and form PHA while under aerobic famine conditions, glycogen is regenerated as a by-product of bacterial growth through the use of PHA. However, unlike PHA which is degraded under famine/aerobic conditions, longer famine/aerobic periods will increase glycogen storage. So a key difference between using PHA versus glycogen for denitrification in post-anoxic or microaerobic conditions is that extended aerobic periods prior to denitrification can deteriorate denitrification potential with PHA as an electron donor versus maximizing denitrification potential with glycogen as an electron donor.
Evidence in Full-Scale Facilities
There have been several recent observations of post-anoxic denitrification without any external carbon addition. Bauhs (2021) showed that approximately 33% of NOx removal in the post-anoxic zone of the VIP+2 process at the Virginia Initiative Plant (VIP) could be attributed to a 'mysterious' denitrification after accounting for denitrification due to methanol and endogenous decay. Furthermore, ex-situ denitrification tests showed that extending the aeration period before the anoxic phase (without addition of carbon) reduced the specific denitrification rates (SDNR) which would implicate PHA as the primary carbon store for post-anoxic denitrification. The Rochester WRP, MN consists of a two-stage high-purity oxygen plant and an activated sludge plant in parallel. A demonstration was conducted at the activated sludge plant which has the ability to be operated in an AO or A2O mode. Low DO operation was achieved by setting DO setpoints for Zone 2 and adjusting airflows proportionally to the other aerated zones (Figure 2). During the course of the demonstration study several strategies for improving total inorganic nitrogen (TIN) removal were tested including the addition of a mixed liquor recycle (MLR) at 1Q and the implementation of a post-anoxic zone (Zone 3) by switching off air and only turning it on twice a day to enable mixing. Without the post-anoxic zone, the process was able to achieve an effluent TIN of ~15 mg-N/L at a DO setpoint of 0.2 mg/L (Figure 3). However, when the post-anoxic zone was implemented, the TIN concentrations at the end of the BNR process decreased from 15 mg-N/L to <10 mg-N/L suggesting the availability of unused carbon (slowly biodegradable COD or internally stored carbon) for denitrification. Profiling for PHA and glycogen and batch activity tests are being conducted to elucidate the source of electrons for denitrification. At the Pueblo CO WRF, comprising a modified Johannesburg configuration with low DO controls and physical selector for sludge densification, a 30Â±6% increase in specific denitrification rates from mixed liquor entering the aerobic zone has been observed when acetate is fed to the anaerobic zone for feast-famine control (Figure 4). Carbon profiling through the anaerobic and anoxic zone indicates complete utilization of the acetate prior to the aerobic zone suggesting an increase in internally stored carbon by storage-product accumulating heterotrophic bacteria as a possible pathway for denitrification (Figure 4).
Process Design
An ideal process design for taking advantage of internal carbon stores (particularly PHA) will take into account the metabolic and regulatory processes that influent PHA accumulation and utilization while accounting for wastewater engineering considerations. Studies have shown that PHA storage is possible in PHA-accumulating OHOs under anoxic and aerobic conditions and the PHA yield is higher under low O2/NO3- supply rates compared to higher supply rates (Beun et al., 2000; Ã‡iÇ§gin et al., 2009; Third et al., 2003). A conceptual framework for process design for PHA-driven denitrification under low DO operation is shown in Figure 5. Figure 6 shows a conceptual framework for why high DO operation is counter-productive to utilization of PHA for denitrification. The full presentation will discuss important features of the mechanism of internal carbon storage and subsequent use for denitrification along with discussions of the microbial ecology of internal carbon storing bacteria. We will also provide evidence of utilization of carbon stores for denitrification using data from multiple facilities that are part of an ongoing Water Research Foundation study (WRF 5083) which will include profiling of glycogen and PHA along with results of batch activity tests. Finally, we will discuss potential design and operational considerations for carbon management and optimal usage of internal carbon storage for BNR.

Total nitrogen removal has historically been limited by the amount of reducing electrons in wastewater to perform denitrification. Using external carbon sources can be expensive especially with stringent nitrogen limits. For this reason, there has been an increased interest in using internally stored carbon for denitrification. This paper summarizes the current state of knowledge on the production and usage of internally stored carbon for BNR along with evidence observed in full-scale facilities.

SpeakerSrinivasan, Varun

Presentation time

14:20:00

14:35:00

Session time

13:30:00

15:00:00

TopicIntermediate Level, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

Srinivasan, Varun

Author(s)Varun Srinivasan1; Ahmed Al-Omari2; Pusker Regmi3; Jose Jimenez4; Mark Miller5; Don Esping6; Kayla Bauhs7; Corey Bjornberg8; Charles Bott9; Belinda Sturm10

Author affiliation(s)Brown & Caldwell, Andover, MA1; Brown & Caldwell, Alexandria, VA 2; Brown & Caldwell, Beltville, MD3; Brown and Caldwell, Orlando, FL 4; Brown & Caldwell, Charlotte, NC 5; Brown and Caldwell, St. Paul, MN6; Brown & Caldwell, Nashville, TN7; Rochester Water Reclamation Plant, Rochester, MN8; Hampton Roads Sanitation District, Virginia Beach, VA9; University of Kansas, Lawrence, KS10

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2022

DOI10.2175/193864718825158651

Volume / Issue

Content sourceWEFTEC

Word count22

A Case for Using Internally Stored Carbon for Intensifying Biological Nutrient Removal â€“ Mechanism, Evidence in Full-Scale Facilities, and Process Design

Abstract

SpeakerSrinivasan, Varun

Presentation time

14:20:00

14:35:00

Session time

13:30:00

15:00:00

TopicIntermediate Level, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

Srinivasan, Varun

Author(s)V. Srinivasan1; A. Al-Omari2; P. Regmi3; J. Jimenez4; M. Miller5; D. Esping6; K. Bauhs7; C. Bjornberg8; C. Bott9; B. Sturm10

Author affiliation(s)1Brown & Caldwell, Andover, MA; 2Brown & Caldwell, Alexandria, VA; 3Brown & Caldwell, Beltville, MD; 4Brown and Caldwell, Orlando, FL; 5Brown & Caldwell, Charlotte, NC; 6Brown and Caldwell, St. Paul, MN; 7Brown & Caldwell, Nashville, TN; 8Rochester Water Reclamation Plant, Rochester, MN; 9Hampton Roads Sanitation District, Virginia Beach, VA10University of Kansas, Lawrence, KS

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct, 2022

DOI10.2175/193864718825158651

Volume / Issue

Content sourceWEFTEC

Word count22

A Practical Sewer Network Digital Twin: It is not a PIPE dream!

Abstract

Introduction Most major municipal areas in the United States are served by a combination of sanitary sewers, separate storm sewers, and combined sewers. With growing populations across urban areas, many sewer utilities are struggling to manage stormwater-related challenges such as flash flooding and combined sewer overflow (CSOs). Furthermore, inflow and infiltration (I&I) issues can cause damage to collection systems and increase the peak flow to wastewater treatment facilities leading to untreated wastewater and storm water being released to the environment including local waterways. This could result in their impairments and risks to public health. Several states have introduced public notification requirements to promote public awareness of sewage pollution with very short timeframes (within hours) for reporting. In order to achieve desired outcomes and get a real-time view of the collection systems, many utilities have invested in sensors and hydraulic models, but it remains a challenge to utilize these investments to comply with the legislation. Proactive utilities are looking to a digital-first resilient strategy which, not only allows them to comply with regulations, but also helps assess system performance, determine effective control strategies and streamline their operations and maintenance activities. Case Studies In this presentation, we will provide examples of sewer network digital twin (DT) (Figure 1) which brings in data from multiple sources (SCADA, IoT sensors, historical rainfall and weather forecasts, GIS, etc.) and uses hydraulic models and data analytics to deliver outcomes for sewer utilities. The presentation will include case studies using implementation of the digital twins for The Metropolitan District Hartford, Connecticut and The City of New Bedford, Massachusetts. This implementation of a sewer network DT platform has enabled the validation of false positive overflow readings through tying in maintenance activity data from CMMS, correct false positive notifications from flow meters by using a hydraulic model to validate overflows and provides a single dashboard combining all relevant data. The platform also uses machine learning to perform sensor data anomaly detection for short-term anomalies such as sensor drop-outs and maintenance-related issues and long-term anomalies such as sensor drift (Figure 2). The platform is able to use hydraulic models and weather forecasts to provide overflow forecasts enabling the utility to perform proactive maintenance and planning (Figure 3). Furthermore, use of hydraulic models in combination with sensor data enables utilities to perform data-driven maintenance activities (Figure 4). Public notifications can be automated through direct integration to the city's website and with a reverse 911 system which includes multilingual support. A capacity assessment tool enables the utility to leverage the hydraulic model to determine available capacity to make decisions on new permit requests. The platform is also able to drive pro-active maintenance by sending notifications to CMMS software. By providing a complete real-time and retrospective view of the sewer system, the sewer network DT can enable utilities to characterize the performance of their sewer network, prioritize maintenance and remediation needs and streamline regulatory reporting in one single platform.

This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.

SpeakerSrinivasan, Varun

Presentation time

14:30:00

15:00:00

Session time

13:30:00

15:00:00

SessionAI and Digital Twins

Session number21

Session locationConnecticut Convention Center, Hartford, Connecticut

TopicArtificial Intelligence, Collection Systems, Digital Twin, Emergency Preparedness And Response, Sensors, Smart Data Infrastructure, Smart Water Infrastructure, SSO Reduction, Utility of the Future, Wet Weather

Author(s)

Srinivasan, Varun

Author(s)V. Srinivasan1, J. Waterbury2, J. Costa3

Author affiliation(s)Trinnex, 1; The Metropolitan District 2; City of New Bedford 3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2024

DOI10.2175/193864718825159380

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count13

A Practical Sewer Network Digital Twin - It is not a PIPE dream

Abstract

Most major municipal areas in the United States are served by a combination of sanitary sewers, separate storm sewers, and combined sewers. With growing populations across urban areas, many sewer utilities are struggling to manage stormwater-related challenges such as flash flooding and combined sewer overflow (CSOs). Furthermore, inflow and infiltration (I&I) issues can cause damage to collection systems and increase the peak flow to wastewater treatment facilities leading to untreated wastewater and storm water being released to the environment including local waterways. This could result in their impairments and risks to public health. Several states have introduced public notification requirements to promote public awareness of sewage pollution with very short timeframes (within hours) for reporting. In order to achieve desired outcomes and get a real-time view of the collection systems, many utilities have invested in sensors and hydraulic models, but it remains a challenge to utilize these investments to comply with the legislation. Proactive utilities are looking to a digital-first resilient strategy which, not only allows them to comply with regulations, but also helps assess system performance, determine effective control strategies and streamline their operations and maintenance activities. In this presentation, we will provide examples of sewer network digital twin (DT) (Figure 1) which brings in data from multiple sources (SCADA, IoT sensors, historical rainfall and weather forecasts, GIS, etc.) and uses hydraulic models and data analytics to perform anomaly detection, compare sensor and model generated information (Figure 2), forecast overflow events based on weather forecasts, comply with local regulations for public notifications and regulatory reporting, and streamline maintenance activities. This implementation of a sewer network DT platform has enabled the validation of false positive overflow readings through tying in maintenance activity data from CMMS, correct false positive notifications from flow meters by using a hydraulic model to validate overflows and provides a single dashboard combining all relevant data. The platform also uses machine learning to perform sensor data anomaly detection for short-term anomalies such as sensor drop-outs and maintenance-related issues and long-term anomalies such as sensor drift (Figure 3). The platform is able to use hydraulic models and weather forecasts to provide overflow forecasts enabling the utility to perform proactive maintenance and planning. Public notifications can be automated through direct integration to the city's website and with a reverse 911 system which includes multilingual support. Furthermore, dynamic real-time inflow and infiltration estimation enables prioritization of pipe rehab and maintenance by targeting problem areas. By providing a complete real-time and retrospective view of the sewer system, the sewer network DT can enable utilities to characterize the performance of their sewer network, prioritize maintenance and remediation needs and streamline regulatory reporting in one single platform.

This paper was presented at the WEF/AWWA Utility Management Conference, February 13-16, 2024.

SpeakerSrinivasan, Varun

Presentation time

15:30:00

17:00:00

Session time

15:30:00

17:00:00

SessionApplications of Data Management and Analysis

Session number28

Session locationOregon Convention Center, Portland, Oregon

TopicData Management & Systems

Author(s)

Srinivasan, Varun

Author(s)V. Srinivasan1, J. Nelson1, S. Drangsholt1, C. Christy1

Author affiliation(s)Trinnex 1;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Feb 2024

DOI10.2175/193864718825159297

Volume / Issue

Content sourceUtility Management Conference

Word count14

Description: Leveraging artificial intelligence to detect sensor issues and operational problems...

Leveraging artificial intelligence to detect sensor issues and operational problems in sewer systems

Abstract

This presentation is a case study on the use of artificial intelligence (AI) to detect issues and operational problems in sewer systems more quickly and reliably by identifying anomalies in sewer flow data, including: -Sudden shifts indicative of issues such as sensor failure or blockages in the system -More gradual changes in the data over longer periods of time, which point to issues like sensor drift or other less immediately obvious operational or environmental problems -Differences between calibrated hydraulic models and sensor data The authors will discuss the application of our AI based approach for detecting anomalies in a large water/wastewater agency located in the midwestern united states. We have found that our AI based approach is highly effective at detecting subtle anomalies such as sensor drift, as well as sudden shifts in the flow data. In some instances, it has detected issues weeks before they would have otherwise been detected during manual review or inspection. Not only is the solution effective, but we also chose an approach that is relatively easy to understand and explain, which makes it more actionable and less challenging to maintain. Through our presentation, the authors intend to highlight the critical role that engineering subject matter expertise played in the formulation of our successful approach, and demonstrate the success that can be achieved through the nexus of data science and engineering.

This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.

SpeakerSrinivasan, Varun

Presentation time

16:15:00

16:45:00

Session time

15:45:00

16:45:00

SessionAsset Management Software

Session number10

Session locationConnecticut Convention Center, Hartford, Connecticut

TopicArtificial Intelligence, Asset Management, Combined Sewer Overflow, Combined Sewer System, Preventative Maintenance, Work Order Management And Scheduling

Author(s)

Deheer, Katie

Author(s)K. Deheer1, V. Srinivasan1

Author affiliation(s)Trinnex 1

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Apr 2024

DOI10.2175/193864718825159364

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count14

Predictive Iron Dosing For Phosphorus Removal: A Data-Driven Strategy

Abstract

Introduction
DC Water owns and operates the Blue Plains Advanced Wastewater Treatment Plant (Blue Plains AWTP) which serves Washington DC and parts of Virginia and Maryland. The Blue Plains AWTP averages 384 mgd treated and has stringent nitrogen and phosphorus limits. The plant performs primary clarification, high-rate carbon removal (secondary treatment) followed by a separate nitrification and denitrification process with methanol addition for nitrogen removal (NiteDenite), effluent filtration and disinfection. Removal of phosphorus is largely accomplished by ferric chloride dosing with three possible dosing locations; upstream of the primary clarifiers, in the secondary treatment process and the NiteDenite process. The effluent phosphorus limits are 0.17 mg/L (average monthly) and 0.34 mg/L (average weekly). With these stringent limits, and the complexity of the process flow, managing iron dosing is crucial for a number of reasons. First, is the occasional overdosing of iron, which may cause P-limitations in the secondary and NiteDenite processes. Second, is the slow response of iron dosing when increasing trends in effluent phosphorus is observed. Finally, due to the experienced increase in ferric costs over the years, Blue Plains AWTP operators and engineers are seeking to optimize the dosing to more efficiencty remove phosphorous and cut down the operational cost. Phosphorus (P) removal using iron salts relies on effective coagulation and flocculation processes. The addition of ferric salts to wastewater results in rapid formation of hydrous ferric oxides (HFO) flocs which then co-precipitate with orthophosphate (OP) while subsequent removal is due to adsorption and complexation. Due to the different mechanisms of P removal and the complex nature of the mechanism, optimization of chemical P removal using iron can be difficult. Furthermore, several factors such as pH, iron dose, mixing conditions, retention time, wastewater composition, and HFO aging can affect P removal. Previous analysis and preliminary modeling by staff at the Blue Plains AWTP suggested auto-correlation of effluent P concentrations with previous week's effluent P concentrations suggesting potential lag in response to ferric dosing. Furthermore, automated control of ferric dosing based on OP analyzers in the influent did not seem to provide any additional benefit based on testing by plant staff. The objective of this study was to use a data-driven approach leveraging advanced analytics, machine learning (ML)-based classification and modeling to provide operational guidance and control strategy for optimizing ferric dosing for chemical P removal to meet effluent discharge limits while stabilizing process performance in secondary and NiteDenite processes while reducing costs.
Methods and Results
Daily monitoring data from 2012-2020 were obtained from the Blue Plains AWTP and uploaded to SeeqÂ®, which is an advanced process analytics platform for time series data. Data cleaning and noise reduction was performed through outlier removal and signal smoothing using the Loess method. Initial analysis showed that the ferric dosing was focused on flow pacing during steady state conditions with manual increases in ferric dosing in response to effluent P spikes (Figure 1). This typically resulted in a delayed response since the effluent P concentrations were lab measured daily data. Influent P spikes did not always result in an increase in effluent P concentration and hence, to avoid overdosing of ferric, manual increases in ferric dosing were employed based on operational experience. Furthermore, there were several influent P spikes (Figure 1) which did not show any corresponding increases in effluent P which makes optimization of ferric dosing a complex exercise. An analysis of P fractions (Particulate P, soluble organic P (SOP) and OP) was undertaken based on measured total phosphorus (TP), total soluble phosphorus (TSP) and OP (Figure 2). This showed that there were several intermittent spikes in effluent P due to spikes in particulate P possibly due to bleeding of solids from the filters, however, this would typically not cause problems with permit violations due to their short-term nature. There were also periods where sustained long-term increases in OP concentrations were observed, which were considered causes of concern since these did not immediately respond to manual increases in ferric dosing at the plant. The overall iron dose (Fe3+:P) ratio can be a key factor in determining efficiency of P removal. A correlational analysis of iron dose (mol:mol) was performed and showed no correlation of iron dose with effluent OP. It also showed that the plant staff have been reducing the iron dose closer to a Fe3+:P ratio of 1 mol/mol in an effort to reduce iron usage and costs (orange points in Figure 3). In order to determine if the dosing location had a significant impact on effluent OP concentrations, the primary Fe: secondary Fe dosing ratio was calculated, and a correlational analysis was performed with effluent OP (Figure 4). The analysis showed no correlation suggesting that primary vs secondary dosing had no change in effluent OP. It also showed that the plant staff had been moving towards a more balanced primary vs secondary dosing approach (Primary Fe:Secondary Fe dose ~ 1) to avoid P-limitations in the B-stage of the process. Preliminary linear modeling was conduced to determine key independent variables controlling effluent P concentrations. Influent TP loads, primary and secondary Fe dose and secondary effluent TSS concentrations were used as predictive variables (Figure 5 and Table 1). Influent TP load (moved forward by 3 days) showed a significant impact (coefficient = 0.014, p < 0.0001) on effluent P concentrations which suggested that effluent P lagged the influent P concentrations by 3 days. Addition of secondary effluent TSS lagged by 7 days showed an increase in predictive capability and had a positive correlation with effluent P (coefficient = 1.18, p < 0.0001). The hypothesis is that secondary effluent TSS is an indicator of colloidal and particulate P entering the NiteDenite process where potential release of P under high SRTs and RAS recycling could result in effluent OP bleed through. The simple linear modeling was able to replicate the baseline effluent P concentrations and some of the peaks, however, it was not able to predict the magnitude and duration of the peaks. Furthermore, preliminary process modeling showed that while it was able overall to predict P removal in the process, the spikes in the OP in the effluent were not simulated. The full presentation will aim to address these gaps using machine learning based modeling and classification approaches to provide predictive capability and operational guidance for ferric dosing. Additional correlations with respect to the change and/or the rate change in effluent OP concentration will be explored to evaluate the impact of several independent variables.

Phosphorus (P) removal using iron salts is a complex coagulation and flocculation process due to which its optimization can be difficult. Advanced predictive modeling and machine learning can be effective at modeling complex and layered interactions and serve as a tool to optimize wastewater treatment processes. In this paper, we summarize an effort in collaboration with DC Water to develop a predictive model to optimize ferric dosing for phosphorus removal at the Blue Plains AWTP.

SpeakerSrinivasan, Varun

Presentation time

13:30:00

13:55:00

Session time

13:30:00

15:00:00

TopicIntermediate Level, Asset Management, Biosolids and Residuals, Facility Operations and Maintenance, Intelligent Water

Author(s)

Srinivasan, Varun

Author(s)Varun Srinivasan1; Ahmed Al-Omari2; Haydee De Clippeleir3; Ryu Suzuki4

Author affiliation(s)Brown & Caldwell, Andover, MA1; Brown and Caldwell, Alexandria, VA2; DC Water & Sewer Authority, Washington, DC3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2022

DOI10.2175/193864718825158592

Volume / Issue

Content sourceWEFTEC

Word count10

Description: Simulating Side-Stream EBPR (S2EBPR) in a Full-Scale Demonstration Study: Modeling...

Simulating Side-Stream EBPR (S2EBPR) in a Full-Scale Demonstration Study: Modeling Approach and Challenges

Abstract

WEFTEC, the largest water event of its kind in North America, offers water quality professionals the best in water quality education and training. With almost 200 technical sessions, workshops, mobile sessions, local facility tours and 1,000+ exhibitors, it is the premier water conference! The WEFTEC technical program is selected through a rigorous, peer-review process, ensuring that attendees experience the highest-quality education. WEFTEC speakers are experts and innovators, leading the way in water quality. The following paper was presented at WEFTEC 2020.

SpeakerSrinivasan, Varun

Presentation time

13:50:00

14:10:00

Session time

13:30:00

15:30:00

SessionWhat's Brewing? Advancements in EBPR

Session number412

TopicFacility Operations and Maintenance, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

V. Srinivasan